文本分类实战（二）—— textCNN 模型

1 大纲概述

　　文本分类这个系列将会有十篇左右，包括基于word2vec预训练的文本分类，与及基于最新的预训练模型（ELMo，BERT等）的文本分类。总共有如下系列：

　　word2vec预训练词向量

　　textCNN 模型

　　charCNN 模型

　　Bi-LSTM 模型

　　Bi-LSTM + Attention 模型

　　RCNN 模型

　　Adversarial LSTM 模型

　　Transformer 模型

　　ELMo 预训练模型

　　BERT 预训练模型

　　jupyter notebook代码均在textClassifier仓库中，python代码在NLP-Project中的text_classfier中。

 

2 数据集

　　数据集为IMDB 电影影评，总共有三个数据文件，在/data/rawData目录下，包括unlabeledTrainData.tsv，labeledTrainData.tsv，testData.tsv。在进行文本分类时须要有标签的数据（labeledTrainData），数据预处理如文本分类实战（一）—— word2vec预训练词向量中同样，预处理后的文件为/data/preprocess/labeledTrain.csv。

 

3 textCNN 模型结构

　　textCNN 能够看做是n-grams的表现形式，textCNN介绍能够看这篇，论文Convolutional Neural Networks for Sentence Classification中提出的三种feature size的卷积核能够认为是对应了3-gram，4-gram和5-gram。总体模型结构以下，先用不一样尺寸（3， 4， 5）的卷积核去提取特征，在进行最大池化，最后将不一样尺寸的卷积核提取的特征拼接在一块儿做为输入到softmax中的特征向量。

　　

 

4 配置训练参数

　　咱们将模型参数，训练参数等都配置在Config类中，方便以后调参。

import os
import csv
import time
import datetime
import random
import json

from collections import Counter
from math import sqrt

import gensim
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score

# 配置参数

class TrainingConfig(object):
    epoches = 5
    evaluateEvery = 100
    checkpointEvery = 100
    learningRate = 0.001
    
class ModelConfig(object):
    embeddingSize = 200
    numFilters = 128

    filterSizes = [2, 3, 4, 5]
    dropoutKeepProb = 0.5
    l2RegLambda = 0.0
    
class Config(object):
    sequenceLength = 200  # 取了全部序列长度的均值
    batchSize = 128
    
    dataSource = "../data/preProcess/labeledTrain.csv"
    
    stopWordSource = "../data/english"
    
    numClasses = 1  # 二分类设置为1，多分类设置为类别的数目
    
    rate = 0.8  # 训练集的比例
    
    training = TrainingConfig()
    
    model = ModelConfig()

    
# 实例化配置参数对象
config = Config()

 

5 生成训练数据

　　1）将数据加载进来，将句子分割成词表示，并去除低频词和停用词。

　　2）将词映射成索引表示，构建词汇-索引映射表，并保存成json的数据格式，以后作inference时能够用到。（注意，有的词可能不在word2vec的预训练词向量中，这种词直接用UNK表示）

　　3）从预训练的词向量模型中读取出词向量，做为初始化值输入到模型中。

　　4）将数据集分割成训练集和测试集

# 数据预处理的类，生成训练集和测试集

class Dataset(object):
    def __init__(self, config):
        self.config = config
        self._dataSource = config.dataSource
        self._stopWordSource = config.stopWordSource  
        
        self._sequenceLength = config.sequenceLength  # 每条输入的序列处理为定长
        self._embeddingSize = config.model.embeddingSize
        self._batchSize = config.batchSize
        self._rate = config.rate
        
        self._stopWordDict = {}
        
        self.trainReviews = []
        self.trainLabels = []
        
        self.evalReviews = []
        self.evalLabels = []
        
        self.wordEmbedding =None
        
        self.labelList = []
        
    def _readData(self, filePath):
        """
        从csv文件中读取数据集
        """
        
        df = pd.read_csv(filePath)
        
        if self.config.numClasses == 1:
            labels = df["sentiment"].tolist()
        elif self.config.numClasses > 1:
            labels = df["rate"].tolist()
            
        review = df["review"].tolist()
        reviews = [line.strip().split() for line in review]

        return reviews, labels
    
    def _labelToIndex(self, labels, label2idx):
        """
        将标签转换成索引表示
        """
        labelIds = [label2idx[label] for label in labels]
        return labelIds
    
    def _wordToIndex(self, reviews, word2idx):
        """
        将词转换成索引
        """
        reviewIds = [[word2idx.get(item, word2idx["UNK"]) for item in review] for review in reviews]
        return reviewIds
        
    def _genTrainEvalData(self, x, y, word2idx, rate):
        """
        生成训练集和验证集
        """
        reviews = []
        for review in x:
            if len(review) >= self._sequenceLength:
                reviews.append(review[:self._sequenceLength])
            else:
                reviews.append(review + [word2idx["PAD"]] * (self._sequenceLength - len(review)))
            
        trainIndex = int(len(x) * rate)
        
        trainReviews = np.asarray(reviews[:trainIndex], dtype="int64")
        trainLabels = np.array(y[:trainIndex], dtype="float32")
        
        evalReviews = np.asarray(reviews[trainIndex:], dtype="int64")
        evalLabels = np.array(y[trainIndex:], dtype="float32")

        return trainReviews, trainLabels, evalReviews, evalLabels
        
    def _genVocabulary(self, reviews, labels):
        """
        生成词向量和词汇-索引映射字典，能够用全数据集
        """
        
        allWords = [word for review in reviews for word in review]
        
        # 去掉停用词
        subWords = [word for word in allWords if word not in self.stopWordDict]
        
        wordCount = Counter(subWords)  # 统计词频
        sortWordCount = sorted(wordCount.items(), key=lambda x: x[1], reverse=True)
        
        # 去除低频词
        words = [item[0] for item in sortWordCount if item[1] >= 5]
        
        vocab, wordEmbedding = self._getWordEmbedding(words)
        self.wordEmbedding = wordEmbedding
        
        word2idx = dict(zip(vocab, list(range(len(vocab)))))
        
        uniqueLabel = list(set(labels))
        label2idx = dict(zip(uniqueLabel, list(range(len(uniqueLabel)))))
        self.labelList = list(range(len(uniqueLabel)))
        
        # 将词汇-索引映射表保存为json数据，以后作inference时直接加载来处理数据
        with open("../data/wordJson/word2idx.json", "w", encoding="utf-8") as f:
            json.dump(word2idx, f)
        
        with open("../data/wordJson/label2idx.json", "w", encoding="utf-8") as f:
            json.dump(label2idx, f)
        
        return word2idx, label2idx
            
    def _getWordEmbedding(self, words):
        """
        按照咱们的数据集中的单词取出预训练好的word2vec中的词向量
        """
        
        wordVec = gensim.models.KeyedVectors.load_word2vec_format("../word2vec/word2Vec.bin", binary=True)
        vocab = []
        wordEmbedding = []
        
        # 添加 "pad" 和 "UNK", 
        vocab.append("PAD")
        vocab.append("UNK")
        wordEmbedding.append(np.zeros(self._embeddingSize))
        wordEmbedding.append(np.random.randn(self._embeddingSize))
        
        for word in words:
            try:
                vector = wordVec.wv[word]
                vocab.append(word)
                wordEmbedding.append(vector)
            except:
                print(word + "不存在于词向量中")
                
        return vocab, np.array(wordEmbedding)
    
    def _readStopWord(self, stopWordPath):
        """
        读取停用词
        """
        
        with open(stopWordPath, "r") as f:
            stopWords = f.read()
            stopWordList = stopWords.splitlines()
            # 将停用词用列表的形式生成，以后查找停用词时会比较快
            self.stopWordDict = dict(zip(stopWordList, list(range(len(stopWordList)))))
            
    def dataGen(self):
        """
        初始化训练集和验证集
        """
        
        # 初始化停用词
        self._readStopWord(self._stopWordSource)
        
        # 初始化数据集
        reviews, labels = self._readData(self._dataSource)
        
        # 初始化词汇-索引映射表和词向量矩阵
        word2idx, label2idx = self._genVocabulary(reviews, labels)
        
        # 将标签和句子数值化
        labelIds = self._labelToIndex(labels, label2idx)
        reviewIds = self._wordToIndex(reviews, word2idx)
        
        # 初始化训练集和测试集
        trainReviews, trainLabels, evalReviews, evalLabels = self._genTrainEvalData(reviewIds, labelIds, word2idx, self._rate)
        self.trainReviews = trainReviews
        self.trainLabels = trainLabels
        
        self.evalReviews = evalReviews
        self.evalLabels = evalLabels
        
        
data = Dataset(config)
data.dataGen()

 

6 生成batch数据集

　　采用生成器的形式向模型输入batch数据集，（生成器能够避免将全部的数据加入到内存中）

# 输出batch数据集

def nextBatch(x, y, batchSize):
        """
        生成batch数据集，用生成器的方式输出
        """
    
        perm = np.arange(len(x))
        np.random.shuffle(perm)
        x = x[perm]
        y = y[perm]
        
        numBatches = len(x) // batchSize

        for i in range(numBatches):
            start = i * batchSize
            end = start + batchSize
            batchX = np.array(x[start: end], dtype="int64")
            batchY = np.array(y[start: end], dtype="float32")
            
            yield batchX, batchY

 

7 textCNN 模型

 

# 构建模型
class TextCNN(object):
    """
    Text CNN 用于文本分类
    """
    def __init__(self, config, wordEmbedding):

        # 定义模型的输入
        self.inputX = tf.placeholder(tf.int32, [None, config.sequenceLength], name="inputX")
        self.inputY = tf.placeholder(tf.int32, [None], name="inputY")
        
        self.dropoutKeepProb = tf.placeholder(tf.float32, name="dropoutKeepProb")
        
        # 定义l2损失
        l2Loss = tf.constant(0.0)
        
        # 词嵌入层
        with tf.name_scope("embedding"):

            # 利用预训练的词向量初始化词嵌入矩阵
            self.W = tf.Variable(tf.cast(wordEmbedding, dtype=tf.float32, name="word2vec") ,name="W")
            # 利用词嵌入矩阵将输入的数据中的词转换成词向量，维度[batch_size, sequence_length, embedding_size]
            self.embeddedWords = tf.nn.embedding_lookup(self.W, self.inputX)
            # 卷积的输入是思惟[batch_size, width, height, channel]，所以须要增长维度，用tf.expand_dims来增大维度
            self.embeddedWordsExpanded = tf.expand_dims(self.embeddedWords, -1)

        # 建立卷积和池化层
        pooledOutputs = []
        # 有三种size的filter，3， 4， 5，textCNN是个多通道单层卷积的模型，能够看做三个单层的卷积模型的融合
        for i, filterSize in enumerate(config.model.filterSizes):
            with tf.name_scope("conv-maxpool-%s" % filterSize):
                # 卷积层，卷积核尺寸为filterSize * embeddingSize，卷积核的个数为numFilters
                # 初始化权重矩阵和偏置
                filterShape = [filterSize, config.model.embeddingSize, 1, config.model.numFilters]
                W = tf.Variable(tf.truncated_normal(filterShape, stddev=0.1), name="W")
                b = tf.Variable(tf.constant(0.1, shape=[config.model.numFilters]), name="b")
                conv = tf.nn.conv2d(
                    self.embeddedWordsExpanded,
                    W,
                    strides=[1, 1, 1, 1],
                    padding="VALID",
                    name="conv")
                
                # relu函数的非线性映射
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
                
                # 池化层，最大池化，池化是对卷积后的序列取一个最大值
                pooled = tf.nn.max_pool(
                    h,
                    ksize=[1, config.sequenceLength - filterSize + 1, 1, 1],  # ksize shape: [batch, height, width, channels]
                    strides=[1, 1, 1, 1],
                    padding='VALID',
                    name="pool")
                pooledOutputs.append(pooled)  # 将三种size的filter的输出一块儿加入到列表中

        # 获得CNN网络的输出长度
        numFiltersTotal = config.model.numFilters * len(config.model.filterSizes)
        
        # 池化后的维度不变，按照最后的维度channel来concat
        self.hPool = tf.concat(pooledOutputs, 3)
        
        # 摊平成二维的数据输入到全链接层
        self.hPoolFlat = tf.reshape(self.hPool, [-1, numFiltersTotal])

        # dropout
        with tf.name_scope("dropout"):
            self.hDrop = tf.nn.dropout(self.hPoolFlat, self.dropoutKeepProb)
       
        # 全链接层的输出
        with tf.name_scope("output"):
            outputW = tf.get_variable(
                "outputW",
                shape=[numFiltersTotal, config.numClasses],
                initializer=tf.contrib.layers.xavier_initializer())
            outputB= tf.Variable(tf.constant(0.1, shape=[config.numClasses]), name="outputB")
            l2Loss += tf.nn.l2_loss(outputW)
            l2Loss += tf.nn.l2_loss(outputB)
            self.logits = tf.nn.xw_plus_b(self.hDrop, outputW, outputB, name="logits")
            if config.numClasses == 1:
                self.predictions = tf.cast(tf.greater_equal(self.logits, 0.0), tf.int32, name="predictions")
            elif config.numClasses > 1:
                self.predictions = tf.argmax(self.logits, axis=-1, name="predictions")
            
            print(self.predictions)
        
        # 计算二元交叉熵损失
        with tf.name_scope("loss"):
            
            if config.numClasses == 1:
                losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits, labels=tf.cast(tf.reshape(self.inputY, [-1, 1]), 
                                                                                                    dtype=tf.float32))
            elif config.numClasses > 1:
                losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=self.inputY)
                
            self.loss = tf.reduce_mean(losses) + config.model.l2RegLambda * l2Loss
            

 

 

8 定义计算metrics的函数

 

"""
定义各种性能指标
"""

def mean(item: list) -> float:
    """
    计算列表中元素的平均值
    :param item: 列表对象
    :return:
    """
    res = sum(item) / len(item) if len(item) > 0 else 0
    return res


def accuracy(pred_y, true_y):
    """
    计算二类和多类的准确率
    :param pred_y: 预测结果
    :param true_y: 真实结果
    :return:
    """
    if isinstance(pred_y[0], list):
        pred_y = [item[0] for item in pred_y]
    corr = 0
    for i in range(len(pred_y)):
        if pred_y[i] == true_y[i]:
            corr += 1
    acc = corr / len(pred_y) if len(pred_y) > 0 else 0
    return acc


def binary_precision(pred_y, true_y, positive=1):
    """
    二类的精确率计算
    :param pred_y: 预测结果
    :param true_y: 真实结果
    :param positive: 正例的索引表示
    :return:
    """
    corr = 0
    pred_corr = 0
    for i in range(len(pred_y)):
        if pred_y[i] == positive:
            pred_corr += 1
            if pred_y[i] == true_y[i]:
                corr += 1

    prec = corr / pred_corr if pred_corr > 0 else 0
    return prec


def binary_recall(pred_y, true_y, positive=1):
    """
    二类的召回率
    :param pred_y: 预测结果
    :param true_y: 真实结果
    :param positive: 正例的索引表示
    :return:
    """
    corr = 0
    true_corr = 0
    for i in range(len(pred_y)):
        if true_y[i] == positive:
            true_corr += 1
            if pred_y[i] == true_y[i]:
                corr += 1

    rec = corr / true_corr if true_corr > 0 else 0
    return rec


def binary_f_beta(pred_y, true_y, beta=1.0, positive=1):
    """
    二类的f beta值
    :param pred_y: 预测结果
    :param true_y: 真实结果
    :param beta: beta值
    :param positive: 正例的索引表示
    :return:
    """
    precision = binary_precision(pred_y, true_y, positive)
    recall = binary_recall(pred_y, true_y, positive)
    try:
        f_b = (1 + beta * beta) * precision * recall / (beta * beta * precision + recall)
    except:
        f_b = 0
    return f_b


def multi_precision(pred_y, true_y, labels):
    """
    多类的精确率
    :param pred_y: 预测结果
    :param true_y: 真实结果
    :param labels: 标签列表
    :return:
    """
    if isinstance(pred_y[0], list):
        pred_y = [item[0] for item in pred_y]

    precisions = [binary_precision(pred_y, true_y, label) for label in labels]
    prec = mean(precisions)
    return prec


def multi_recall(pred_y, true_y, labels):
    """
    多类的召回率
    :param pred_y: 预测结果
    :param true_y: 真实结果
    :param labels: 标签列表
    :return:
    """
    if isinstance(pred_y[0], list):
        pred_y = [item[0] for item in pred_y]

    recalls = [binary_recall(pred_y, true_y, label) for label in labels]
    rec = mean(recalls)
    return rec


def multi_f_beta(pred_y, true_y, labels, beta=1.0):
    """
    多类的f beta值
    :param pred_y: 预测结果
    :param true_y: 真实结果
    :param labels: 标签列表
    :param beta: beta值
    :return:
    """
    if isinstance(pred_y[0], list):
        pred_y = [item[0] for item in pred_y]

    f_betas = [binary_f_beta(pred_y, true_y, beta, label) for label in labels]
    f_beta = mean(f_betas)
    return f_beta


def get_binary_metrics(pred_y, true_y, f_beta=1.0):
    """
    获得二分类的性能指标
    :param pred_y:
    :param true_y:
    :param f_beta:
    :return:
    """
    acc = accuracy(pred_y, true_y)
    recall = binary_recall(pred_y, true_y)
    precision = binary_precision(pred_y, true_y)
    f_beta = binary_f_beta(pred_y, true_y, f_beta)
    return acc, recall, precision, f_beta


def get_multi_metrics(pred_y, true_y, labels, f_beta=1.0):
    """
    获得多分类的性能指标
    :param pred_y:
    :param true_y:
    :param labels:
    :param f_beta:
    :return:
    """
    acc = accuracy(pred_y, true_y)
    recall = multi_recall(pred_y, true_y, labels)
    precision = multi_precision(pred_y, true_y, labels)
    f_beta = multi_f_beta(pred_y, true_y, labels, f_beta)
    return acc, recall, precision, f_beta

 

 

9 训练模型

　　在训练时，咱们定义了tensorBoard的输出，并定义了两种模型保存的方法。　

# 训练模型

# 生成训练集和验证集
trainReviews = data.trainReviews
trainLabels = data.trainLabels
evalReviews = data.evalReviews
evalLabels = data.evalLabels

wordEmbedding = data.wordEmbedding
labelList = data.labelList

# 定义计算图
with tf.Graph().as_default():

    session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
    session_conf.gpu_options.allow_growth=True
    session_conf.gpu_options.per_process_gpu_memory_fraction = 0.9  # 配置gpu占用率  

    sess = tf.Session(config=session_conf)
    
    # 定义会话
    with sess.as_default():
        cnn = TextCNN(config, wordEmbedding)
        
        globalStep = tf.Variable(0, name="globalStep", trainable=False)
        # 定义优化函数，传入学习速率参数
        optimizer = tf.train.AdamOptimizer(config.training.learningRate)
        # 计算梯度,获得梯度和变量
        gradsAndVars = optimizer.compute_gradients(cnn.loss)
        # 将梯度应用到变量下，生成训练器
        trainOp = optimizer.apply_gradients(gradsAndVars, global_step=globalStep)
        
        # 用summary绘制tensorBoard
        gradSummaries = []
        for g, v in gradsAndVars:
            if g is not None:
                tf.summary.histogram("{}/grad/hist".format(v.name), g)
                tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
        
        outDir = os.path.abspath(os.path.join(os.path.curdir, "summarys"))
        print("Writing to {}\n".format(outDir))
        
        lossSummary = tf.summary.scalar("loss", cnn.loss)
        summaryOp = tf.summary.merge_all()
        
        trainSummaryDir = os.path.join(outDir, "train")
        trainSummaryWriter = tf.summary.FileWriter(trainSummaryDir, sess.graph)
        
        evalSummaryDir = os.path.join(outDir, "eval")
        evalSummaryWriter = tf.summary.FileWriter(evalSummaryDir, sess.graph)
        
        
        # 初始化全部变量
        saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
        
        # 保存模型的一种方式，保存为pb文件
        savedModelPath = "../model/textCNN/savedModel"
        if os.path.exists(savedModelPath):
            os.rmdir(savedModelPath)
        builder = tf.saved_model.builder.SavedModelBuilder(savedModelPath)
            
        sess.run(tf.global_variables_initializer())

        def trainStep(batchX, batchY):
            """
            训练函数
            """   
            feed_dict = {
              cnn.inputX: batchX,
              cnn.inputY: batchY,
              cnn.dropoutKeepProb: config.model.dropoutKeepProb
            }
            _, summary, step, loss, predictions = sess.run(
                [trainOp, summaryOp, globalStep, cnn.loss, cnn.predictions],
                feed_dict)
            timeStr = datetime.datetime.now().isoformat()
            
            if config.numClasses == 1:
                acc, recall, prec, f_beta = get_binary_metrics(pred_y=predictions, true_y=batchY)

                
            elif config.numClasses > 1:
                acc, recall, prec, f_beta = get_multi_metrics(pred_y=predictions, true_y=batchY,
                                                              labels=labelList)
                
            trainSummaryWriter.add_summary(summary, step)
            
            return loss, acc, prec, recall, f_beta

        def devStep(batchX, batchY):
            """
            验证函数
            """
            feed_dict = {
              cnn.inputX: batchX,
              cnn.inputY: batchY,
              cnn.dropoutKeepProb: 1.0
            }
            summary, step, loss, predictions = sess.run(
                [summaryOp, globalStep, cnn.loss, cnn.predictions],
                feed_dict)
            
            if config.numClasses == 1:
            
                acc, precision, recall, f_beta = get_binary_metrics(pred_y=predictions, true_y=batchY)
            elif config.numClasses > 1:
                acc, precision, recall, f_beta = get_multi_metrics(pred_y=predictions, true_y=batchY, labels=labelList)
            
            evalSummaryWriter.add_summary(summary, step)
            
            return loss, acc, precision, recall, f_beta
        
        for i in range(config.training.epoches):
            # 训练模型
            print("start training model")
            for batchTrain in nextBatch(trainReviews, trainLabels, config.batchSize):
                loss, acc, prec, recall, f_beta = trainStep(batchTrain[0], batchTrain[1])
                
                currentStep = tf.train.global_step(sess, globalStep) 
                print("train: step: {}, loss: {}, acc: {}, recall: {}, precision: {}, f_beta: {}".format(
                    currentStep, loss, acc, recall, prec, f_beta))
                if currentStep % config.training.evaluateEvery == 0:
                    print("\nEvaluation:")
                    
                    losses = []
                    accs = []
                    f_betas = []
                    precisions = []
                    recalls = []
                    
                    for batchEval in nextBatch(evalReviews, evalLabels, config.batchSize):
                        loss, acc, precision, recall, f_beta = devStep(batchEval[0], batchEval[1])
                        losses.append(loss)
                        accs.append(acc)
                        f_betas.append(f_beta)
                        precisions.append(precision)
                        recalls.append(recall)
                        
                    time_str = datetime.datetime.now().isoformat()
                    print("{}, step: {}, loss: {}, acc: {},precision: {}, recall: {}, f_beta: {}".format(time_str, currentStep, mean(losses), 
                                                                                                       mean(accs), mean(precisions),
                                                                                                       mean(recalls), mean(f_betas)))
                    
                if currentStep % config.training.checkpointEvery == 0:
                    # 保存模型的另外一种方法，保存checkpoint文件
                    path = saver.save(sess, "../model/textCNN/model/my-model", global_step=currentStep)
                    print("Saved model checkpoint to {}\n".format(path))
                    
        inputs = {"inputX": tf.saved_model.utils.build_tensor_info(cnn.inputX),
                  "keepProb": tf.saved_model.utils.build_tensor_info(cnn.dropoutKeepProb)}

        outputs = {"predictions": tf.saved_model.utils.build_tensor_info(cnn.predictions)}

        prediction_signature = tf.saved_model.signature_def_utils.build_signature_def(inputs=inputs, outputs=outputs,
                                                                                      method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
        legacy_init_op = tf.group(tf.tables_initializer(), name="legacy_init_op")
        builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING],
                                            signature_def_map={"predict": prediction_signature}, legacy_init_op=legacy_init_op)

        builder.save()

 

10 预测代码

x = "this movie is full of references like mad max ii the wild one and many others the ladybug´s face it´s a clear reference or tribute to peter lorre this movie is a masterpiece we´ll talk much more about in the future"

# 注：下面两个词典要保证和当前加载的模型对应的词典是一致的
with open("../data/wordJson/word2idx.json", "r", encoding="utf-8") as f:
    word2idx = json.load(f)
        
with open("../data/wordJson/label2idx.json", "r", encoding="utf-8") as f:
    label2idx = json.load(f)
idx2label = {value: key for key, value in label2idx.items()}
    
xIds = [word2idx.get(item, word2idx["UNK"]) for item in x.split(" ")]
if len(xIds) >= config.sequenceLength:
    xIds = xIds[:config.sequenceLength]
else:
    xIds = xIds + [word2idx["PAD"]] * (config.sequenceLength - len(xIds))

graph = tf.Graph()
with graph.as_default():
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
    session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options)
    sess = tf.Session(config=session_conf)

    with sess.as_default():
        checkpoint_file = tf.train.latest_checkpoint("../model/textCNN/model/")
        saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
        saver.restore(sess, checkpoint_file)

        # 得到须要喂给模型的参数，输出的结果依赖的输入值
        inputX = graph.get_operation_by_name("inputX").outputs[0]
        dropoutKeepProb = graph.get_operation_by_name("dropoutKeepProb").outputs[0]

        # 得到输出的结果
        predictions = graph.get_tensor_by_name("output/predictions:0")

        pred = sess.run(predictions, feed_dict={inputX: [xIds], dropoutKeepProb: 1.0})[0]
        
pred = [idx2label[item] for item in pred]     
print(pred)

 

11 总结

　　构建tensorflow深度学习模型时，最好能有一套本身的代码框架，上述代码就是我比较喜欢的框架，总共有四大类：参数配置，训练数据生成，模型结构，训练模型。在以后构建其余的模型时，依照这种结构能够很快地实现。建议你们寻找最适合本身的代码结构，不少时候均可以实现代码的复用。